https://github.com/aisuko/generative-ai
The notebooks for generative AI by using PyTorch, Huggingface/diffusers, transforms. And the implementing of the algorithms in paper
Science Score: 23.0%
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○CITATION.cff file
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✓codemeta.json file
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○.zenodo.json file
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✓Academic publication links
Links to: arxiv.org -
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○Scientific vocabulary similarity
Low similarity (8.8%) to scientific vocabulary
Keywords
Repository
The notebooks for generative AI by using PyTorch, Huggingface/diffusers, transforms. And the implementing of the algorithms in paper
Basic Info
- Host: GitHub
- Owner: Aisuko
- License: apache-2.0
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://www.kaggle.com/aisuko/code
- Size: 17.4 MB
Statistics
- Stars: 13
- Watchers: 0
- Forks: 5
- Open Issues: 2
- Releases: 0
Topics
Metadata Files
README.md
PyTorch Fundamentals
Learn the fundamentals of deep learning with PyTorch! This beginner friendly learning path will introduce key concepts to building machine learning models in multiple domains include speech, vision, and natural language processing.
- Basic Python knowledge
- Basic knowledge about how to use Jupyter Notebooks
- Basic understanding of machine learning
And if you are interested to know more, please check another repo Implementation for the different ML tasks on Kaggle platform with GPUs.
NOTE: There do have many bugs due to the different version of dependencies, please open new issue to discuss it.
Introduce to PyTorch
|No|Title|Open in Sagemaker|Open in Kaggle|
|---|---|---|---|
|1|What are Tensors?|
|
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|2|Loading and normalizing datasets|||
|3|Building the model layers|||
|4|Automatic differentiation|||
|5|About the optimization loop|||
|6|Load and run model predictions|||
|7|The full model building process|||
Audio classification with PyTorch
|No|Title|Open in SageMaker|Open in Kaggle|
|---|---|---|---|
|1|Understand audio data and concepts|||
|2|Audio transforms and visualizations|||
Natural language processing with PyTorch
|No|Title|Open in SageMaker|Open in Kaggle|
|---|---|---|---|
|1|Representing text as Tensors|||
|2|Represent words with embeddings|||
|3|Capture patterns with RNN|||
|4|Generate text with RNN|||
Computer vision with PyTorch
|No|Title|Open in SageMaker|Open in Kaggle|
|---|---|---|---|
|1|Introduction to CV with PyTorch|||
|2|Training a simple sense neural network|||
|3|Convolutional Neural Networks|||
|4|Multilayer Dense Neural Network|||
|5|Pre-trained models and transfer learning|||
|6|Lightweight Networks and MobileNet|||
Diffusion
|No|Title|Open in SageMaker|Open in Kaggle|Open in Colab|
|---|---|---|---|---|
|1|Deconstruct the Stable Diffusion pipeline|||
|
|2|Basic training model||||
|3|Deconstruct the basic pipeline||||
|4|Details for models and schedulers||||
|5|Effective and Efficient diffusion||||
|6|Generting by using float16(sppeding up)||||
|7|Stable Diffusion v1.5 demo||||
|8|Load checkpoints models and schedulers||||
|9|Schedulers Performance||||
|10|Stable diffusion with diffusers||||
Paper implementation
|No|Title|Open in SageMaker|Open in Kaggle|Open in Colab|Paper|
|---|---|---|---|---|---|
|1|The annotated diffusion model||||1503.03585
1907.05600
2006.11239|
|2|QLoRA Fine-tuning for Falcon-7B with PEFT|||||
On macOS
All the notebooks are support mps, except if the notebooks import fp16 speeding up:
Contributing
Warm welcome for any contributions, please follow the contributing guidelines.
Acknowledgement
Owner
- Name: Bowen
- Login: Aisuko
- Kind: user
- Location: Global
- Company: RMIT
- Twitter: AisukoLi
- Repositories: 70
- Profile: https://github.com/Aisuko
Member of the GNU Hurd | previously @rancher | Founder of @SkywardAI | PhD candidate at RMIT
GitHub Events
Total
- Watch event: 4
Last Year
- Watch event: 4
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 2
- Total pull requests: 13
- Average time to close issues: N/A
- Average time to close pull requests: about 1 month
- Total issue authors: 1
- Total pull request authors: 5
- Average comments per issue: 1.5
- Average comments per pull request: 0.0
- Merged pull requests: 9
- Bot issues: 0
- Bot pull requests: 6
Past Year
- Issues: 0
- Pull requests: 5
- Average time to close issues: N/A
- Average time to close pull requests: about 2 hours
- Issue authors: 0
- Pull request authors: 2
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 5
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- Aisuko (2)
Pull Request Authors
- imgbot[bot] (5)
- Aisuko (5)
- Tinny-Robot (2)
- cbh778899 (2)
- stack-file[bot] (2)